Condition based maintenance: a survey

Author:

Prajapati Ashok,Bechtel James,Ganesan Subramaniam

Abstract

PurposeThe purpose of this paper is to provide a brief overview of condition based maintenance (CBM) with definitions of various terms, overview of some history, recent developments, applications, and research challenges in the CBM domain.Design/methodology/approachThe article presents the insight into various maintenance strategies and provides their respective merits and demerits in various aspects. It then provides the detailed discussion of CBM that includes applications of various methodologies and technologies that are being implemented in the field. Finally, it ends with open challenges in implementing condition based maintenance systems.FindingsThis paper surveys research articles and describes how CBM can be used to optimize maintenance strategies and increase the feasibility and practicality of a CBM system.Practical implicationsCBM systems are completely practical to implement and applicable to various domains including automotive, manufacturing, aviation, medical, etc. This paper presents a brief overview of literature on CBM and an insight into CBM as a maintenance strategy. CBM has wide applications in automotive, aviation, manufacturing, defense, and other industries. It involves various disciplines like data mining, artificial intelligence, and statistics to enable the systems to be maintenance intelligent. These disciplines help in predicting the future consequences based on the past and current system conditions. Based on the authors’ studies, implementation of such a system is easy and cost effective because it uses existing subsystems to collect statistical data. On top of that it requires building a software layer to process the data and to implement the prognosis techniques in the form of algorithms.Social implicationsThe design of CBM systems highly impact the society in terms of maintenance cost (i.e. reduces the maintenance cost of automobiles, safety by providing real time reporting of the fault using prognosis).Originality/valueTo the best of the authors’ knowledge, this paper is first of its kind in the literature which presents several maintenance strategies and provides a number of possible research directions listed in open research challenges.

Publisher

Emerald

Subject

Industrial and Manufacturing Engineering,Strategy and Management,Safety, Risk, Reliability and Quality

Reference36 articles.

1. Angel, S.J., Gilmartin, B.J., Bongort, K. and Hess, A. (2000), “Prognostics, the real issues involved with predicting life remaining”, Proceedings of IEEE Aerospace Conference, Vol. 6, pp. 457‐69.

2. Barajas, L. and Srinivasa, N. (2008), “Practical approaches for real time diagnostics, prognostics, health management for large scale manufacturing systems”, Proceedings of ASME International Conference, Evanston, IL, available at: http://dx.doi.org/10.1115/MSEC‐ICMP2008‐72511 (accessed September 21, 2010).

3. Baruah, P., Babu, R. and Filev, D. (2006), “An autonomous diagnostics and prognostics framework for condition based maintenance”, Neural Network, International Joint Conference, Vancouver, BC, July, pp. 3428‐35.

4. CBM (2008), DoD Guide Book, Chapet 2, pp. 2‐3, available at: www.acq.osd.mil/log/mpp/cbm+/CBM_DoD_Guidebook_May08.pdf (accessed May 28, 2010).

5. Chen, D. and Trivedi, K.S. (2001), “Analysis of preventive maintenance with general component failure distribution”, on Dependable Computing, Proceeding of Eighth Pacific Rim International Symposium, December, Seoul, pp. 103‐7.

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